Initial QC on RD7 Essential Data - Multi-UTR Genes
Mervin M Fansler
18 November 2022
Purpose
Here we perform a second pass look at the data, similar to the first pass, but focusing specifically on multi-UTR genes.
Initialization
Libraries
library(magrittr)
library(tidyverse)
library(plotly)
library(ggbeeswarm)
library(Matrix)
library(matrixStats)
library(SingleCellExperiment)Parameters
set.seed(20210818)
FILE_SCE_TX="data/sce/rd7_essential.annot.txs.Rds"
## KNOWN FACTORS
GENES_CPSF <- c("CPSF1", "CPSF2", "CPSF3", "CPSF4", "WDR33", "FIP1L1")
GENES_CF1 <- c("NUDT21", "CPSF6", "CPSF7")
GENES_CF2 <- c("PCF11", "CLP1")
GENES_CSTF <- c("CSTF1", "CSTF2", "CSTF2T", "CSTF3")
GENES_PAF <- c("PAF1", "CTR9", "RTF1")
GENES_NEF <- c("NXF1", "THOC1", "THOC2", "THOC3", "THOC5", "THOC6", "THOC7")
GENES_SPL <- str_c("SRSF", 1:11)
GENES_MTOR <- c("MTOR", "RPTOR", "MLST8", "CLK2", "AKT1")
COLOR_MAP <- c("non-targeting"="grey",
"CPSF complex"="red4",
"CFI complex"="purple2",
"CFII complex"="orange2",
"CSTF complex"="peachpuff",
"PAF complex"="steelblue",
"mRNA export factors"="orchid2",
"splicing factors"="seagreen1",
"MTOR complex"="cyan",
"other"="black")Data
Loading
sce <- readRDS(FILE_SCE_TX) %>% `[`(rowData(.)$utr_type_raw == 'multi',)Preprocessing
Among our statistics, we will compute the percentage of UMIs corresponding to specific isoform classes, including IPA, proximal, and distal, in multi-UTR genes.
df_cells <- colData(sce) %>% as_tibble %>%
mutate(umis_total=colSums(counts(sce)),
pct_ipa=colSums(counts(sce[rowData(sce)$is_ipa,]))/umis_total,
pct_proximal=colSums(counts(sce[rowData(sce)$is_proximal,]))/umis_total,
pct_distal=colSums(counts(sce[rowData(sce)$is_distal,]))/umis_total)Target Analysis
We will explore whether any perturbations have effects on basic transcriptome characteristics. These will also be compared against each other.
df_targets <- df_cells %>%
group_by(target_gene, target_gene_id, sgID_AB) %>%
summarize(n_cells=dplyr::n(),
mean_umis_total=mean(umis_total),
pct_ipa=weighted.mean(pct_ipa, umis_total),
pct_proximal=weighted.mean(pct_proximal, umis_total),
pct_distal=weighted.mean(pct_distal, umis_total), .groups='drop') %>%
mutate(gene_set=case_when(
target_gene == "non-targeting" ~ "non-targeting",
target_gene %in% GENES_CPSF ~ "CPSF complex",
target_gene %in% GENES_CF1 ~ "CFI complex",
target_gene %in% GENES_CF2 ~ "CFII complex",
target_gene %in% GENES_CSTF ~ "CSTF complex",
target_gene %in% GENES_PAF ~ "PAF complex",
target_gene %in% GENES_NEF ~ "mRNA export factors",
target_gene %in% GENES_SPL ~ "splicing factors",
target_gene %in% GENES_MTOR ~ "MTOR complex",
TRUE ~ "other"
) %>% factor(levels=c("other", "non-targeting", "CPSF complex",
"CFI complex", "CFII complex", "CSTF complex",
"PAF complex", "mRNA export factors",
"splicing factors", "MTOR complex"))) %>%
mutate(is_known=!gene_set %in% c("non-targeting", "other"))IPA Extremes
df_targets %>%
select(target_gene, n_cells, pct_ipa, mean_umis_total, sgID_AB) %>%
slice_max(pct_ipa, n=20)## # A tibble: 20 × 5
## target_gene n_cells pct_ipa mean_umis_total sgID_AB
## <chr> <int> <dbl> <dbl> <chr>
## 1 HSPA5 75 0.208 8014. HSPA5_+_128003614.23-P1P2|HSPA5_…
## 2 SRP68 55 0.208 6097. SRP68_+_74068540.23-P1P2|SRP68_-…
## 3 EIF3D 55 0.206 14107. EIF3D_+_36925166.23-P1P2|EIF3D_-…
## 4 SNRNP200 101 0.203 9093. SNRNP200_+_96971234.23-P1P2|SNRN…
## 5 ALG11 21 0.203 5103. ALG11_+_52586530.23-P1P2|ALG11_-…
## 6 PRPF6 62 0.201 8243. PRPF6_-_62612504.23-P1P2|PRPF6_+…
## 7 CHMP2A 10 0.201 12971. CHMP2A_-_59066282.23-P1P2|CHMP2A…
## 8 MFAP1 59 0.200 9079. MFAP1_+_44116853.23-P1P2|MFAP1_-…
## 9 PRPF8 89 0.200 9572. PRPF8_+_1588005.23-P1P2|PRPF8_+_…
## 10 PRPF31 156 0.199 9624. PRPF31_-_54619172.23-P1P2|PRPF31…
## 11 CDC5L 71 0.199 13840. CDC5L_-_44355475.23-P1P2|CDC5L_+…
## 12 SMU1 82 0.198 10946. SMU1_+_33076608.23-P1P2|SMU1_-_3…
## 13 SNIP1 46 0.198 7918. SNIP1_+_38019681.23-P1P2|SNIP1_+…
## 14 TFPT 8 0.198 6258. TFPT_+_54619005.23-P1P2|TFPT_+_5…
## 15 LSM6 47 0.198 9532. LSM6_-_147096941.23-P1P2|LSM6_-_…
## 16 DHDDS 60 0.198 6487. DHDDS_-_26758867.23-P1P2|DHDDS_-…
## 17 EIF3E 50 0.198 14643. EIF3E_+_109260706.23-P1P2|EIF3E_…
## 18 EIF2S1 83 0.197 5264. EIF2S1_-_67827085.23-P1P2|EIF2S1…
## 19 PRPF4 85 0.197 11623. PRPF4_-_116037989.23-P1P2|PRPF4_…
## 20 PRPF3 113 0.197 10797. PRPF3_-_150294051.23-P1P2|PRPF3_…df_targets %>%
select(target_gene, n_cells, pct_ipa, mean_umis_total, sgID_AB) %>%
slice_min(pct_ipa, n=20)## # A tibble: 20 × 5
## target_gene n_cells pct_ipa mean_umis_total sgID_AB
## <chr> <int> <dbl> <dbl> <chr>
## 1 CPSF4 32 0.164 9121. CPSF4_-_99036611.23-P1P2|CPSF4_-…
## 2 RNMT 71 0.167 8433. RNMT_-_13726752.23-P1P2|RNMT_+_1…
## 3 FIP1L1 13 0.167 8796. FIP1L1_+_54243867.23-P1P2|FIP1L1…
## 4 RAMAC 61 0.167 9286. FAM103A1_-_83654987.23-P1P2|FAM1…
## 5 CPSF1 50 0.167 9195. CPSF1_+_145634685.23-P1P2|CPSF1_…
## 6 SERF2 33 0.167 9952. SERF2_+_44084560.23-P1|SERF2_+_4…
## 7 CNOT1 99 0.168 15223. CNOT1_+_58663696.23-P1P2|CNOT1_-…
## 8 CSTF3 67 0.169 7318. CSTF3_+_33183038.23-P1P2|CSTF3_-…
## 9 PTCD1 71 0.169 9058. PTCD1_-_99036387.23-P2|PTCD1_+_9…
## 10 NKX6-1 38 0.169 5556. NKX6-1_-_85419268.23-P1P2|NKX6-1…
## 11 LAMB1 14 0.170 12594. LAMB1_-_107643640.23-P1P2|LAMB1_…
## 12 CNOT2 67 0.170 13224. CNOT2_-_70637260.23-P1P2|CNOT2_+…
## 13 ZC3H18 66 0.170 8507. ZC3H18_+_88636860.23-P1P2|ZC3H18…
## 14 ELOB 134 0.170 8282. TCEB2_+_2827236.23-P1P2|TCEB2_+_…
## 15 CNOT3 59 0.171 17582. CNOT3_+_54641532.23-P1P2|CNOT3_-…
## 16 TPRKB 134 0.171 8855. TPRKB_+_73964387.23-P1P2|TPRKB_-…
## 17 ARRB2 13 0.171 12407. ARRB2_-_4614022.23-P1P2|ARRB2_-_…
## 18 XRN1 51 0.171 11324. XRN1_+_142166856.23-P1P2|XRN1_-_…
## 19 PCBP1 205 0.171 10528. PCBP1_+_70314830.23-P1P2|PCBP1_+…
## 20 YAE1 40 0.172 9186. YAE1D1_+_39606256.23-P1P2|YAE1D1…Proximal Extemes
df_targets %>%
select(target_gene, n_cells, pct_proximal, mean_umis_total, sgID_AB) %>%
slice_max(pct_proximal, n=20)## # A tibble: 20 × 5
## target_gene n_cells pct_proximal mean_umis_total sgID_AB
## <chr> <int> <dbl> <dbl> <chr>
## 1 UPF1 46 0.0930 11877. UPF1_+_18942771.23-P1P2|UPF…
## 2 SMG5 468 0.0929 10472. SMG5_+_156252585.23-P1P2|SM…
## 3 OGFOD1 55 0.0924 9166. OGFOD1_-_56485723.23-P1P2|O…
## 4 NUDT21 17 0.0916 9848. NUDT21_+_56485197.23-P1P2|N…
## 5 CLOCK 542 0.0916 10382. CLOCK_-_56412950.23-P1P2|CL…
## 6 CPSF6 27 0.0914 8465. CPSF6_+_69633592.23-P1P2|CP…
## 7 INTS10 91 0.0903 9816. INTS10_+_19675000.23-P1P2|I…
## 8 XRN1 51 0.0897 11324. XRN1_+_142166856.23-P1P2|XR…
## 9 ARID5B 118 0.0895 9692. ARID5B_-_63660959.23-ENST00…
## 10 TADA2A 119 0.0892 8798. TADA2A_-_35767029.23-P1P2|T…
## 11 PET117 155 0.0891 9082. PET117_-_18118759.23-P1P2|P…
## 12 UPF2 113 0.0890 11715. UPF2_+_12084799.23-P1P2|UPF…
## 13 SMG1 48 0.0889 10309. SMG1_+_18937294.23-P1P2|SMG…
## 14 FBXO42 117 0.0886 8980. FBXO42_+_16678481.23-P1P2|F…
## 15 S100A1 112 0.0883 9202. S100A1_-_153603069.23-P2|S1…
## 16 PPRC1 55 0.0880 6205. PPRC1_-_103892809.23-P1P2|P…
## 17 GYG1 294 0.0879 9411. GYG1_+_148709356.23-P1P2|GY…
## 18 DHX36 72 0.0878 7691. DHX36_+_154042223.23-P1P2|D…
## 19 SMG7 143 0.0878 10090. SMG7_+_183441690.23-P1P2|SM…
## 20 CMTR1 82 0.0878 7524. CMTR1_-_37401025.23-P1P2|CM…df_targets %>%
select(target_gene, n_cells, pct_proximal, mean_umis_total, sgID_AB) %>%
slice_min(pct_proximal, n=20)## # A tibble: 20 × 5
## target_gene n_cells pct_proximal mean_umis_total sgID_AB
## <chr> <int> <dbl> <dbl> <chr>
## 1 CEBPG 12 0.0671 10512. CEBPG_-_33864240.23-P1P2|CE…
## 2 ANLN 5 0.0674 17146. ANLN_-_36429483.23-P1P2|ANL…
## 3 PRPF6 62 0.0688 8243. PRPF6_-_62612504.23-P1P2|PR…
## 4 SNRNP200 101 0.0691 9093. SNRNP200_+_96971234.23-P1P2…
## 5 PRPF31 156 0.0695 9624. PRPF31_-_54619172.23-P1P2|P…
## 6 SMU1 82 0.0698 10946. SMU1_+_33076608.23-P1P2|SMU…
## 7 PRPF3 113 0.0703 10797. PRPF3_-_150294051.23-P1P2|P…
## 8 RBM42 49 0.0705 8731. RBM42_-_36120011.23-P1P2|RB…
## 9 PRPF40A 45 0.0707 11712. PRPF40A_+_153573726.23-P1P2…
## 10 EIF3D 55 0.0707 14107. EIF3D_+_36925166.23-P1P2|EI…
## 11 LSM5 73 0.0708 10218. LSM5_-_32529957.23-P1|LSM5_…
## 12 AP2A1 14 0.0709 9764. AP2A1_-_50270622.23-P1P2|AP…
## 13 EIF5 14 0.0710 6649. EIF5_-_103800558.23-P1|EIF5…
## 14 ZMAT2 183 0.0710 11950. ZMAT2_-_140080084.23-P1|ZMA…
## 15 INCENP 4 0.0710 13120. INCENP_+_61891519.23-P1P2|I…
## 16 EFTUD2 104 0.0711 10176. EFTUD2_+_42976772.23-P1P2|E…
## 17 LSM4 80 0.0712 10419. LSM4_+_18433820.23-P1P2|LSM…
## 18 MFAP1 59 0.0712 9079. MFAP1_+_44116853.23-P1P2|MF…
## 19 KIF23 11 0.0713 17233. KIF23_+_69706676.23-P1P2|KI…
## 20 PRPF8 89 0.0713 9572. PRPF8_+_1588005.23-P1P2|PRP…Distal Extremes
df_targets %>%
select(target_gene, n_cells, pct_distal, mean_umis_total, sgID_AB) %>%
slice_max(pct_distal, n=20)## # A tibble: 20 × 5
## target_gene n_cells pct_distal mean_umis_total sgID_AB
## <chr> <int> <dbl> <dbl> <chr>
## 1 NKX6-1 38 0.445 5556. NKX6-1_-_85419268.23-P1P2|NKX…
## 2 FIP1L1 13 0.442 8796. FIP1L1_+_54243867.23-P1P2|FIP…
## 3 SERF2 33 0.442 9952. SERF2_+_44084560.23-P1|SERF2_…
## 4 CPSF4 32 0.441 9121. CPSF4_-_99036611.23-P1P2|CPSF…
## 5 TAF2 67 0.436 5979. TAF2_-_120844853.23-P1P2|TAF2…
## 6 TAF12 13 0.435 5564. TAF12_+_28969568.23-P1P2|TAF1…
## 7 CPSF1 50 0.434 9195. CPSF1_+_145634685.23-P1P2|CPS…
## 8 TAF10 27 0.434 8729. TAF10_-_6633436.23-P1P2|TAF10…
## 9 ANLN 5 0.433 17146. ANLN_-_36429483.23-P1P2|ANLN_…
## 10 CEBPG 12 0.433 10512. CEBPG_-_33864240.23-P1P2|CEBP…
## 11 TAF7 49 0.433 7705. TAF7_+_140700244.23-P1P2|TAF7…
## 12 MRPS34 70 0.433 7631. MRPS34_+_1823083.23-P1P2|MRPS…
## 13 FUNDC2 44 0.433 5875. FUNDC2_-_154255407.23-P1P2|FU…
## 14 TAF8 76 0.433 5751. TAF8_-_42018330.23-P1P2|TAF8_…
## 15 SRRT 84 0.433 8787. SRRT_+_100472814.23-P1P2|SRRT…
## 16 HINFP 10 0.432 15343. HINFP_-_118992315.23-P1P2|HIN…
## 17 MRPS14 32 0.432 11719. MRPS14_-_174992444.23-P1P2|MR…
## 18 PRPF40A 45 0.432 11712. PRPF40A_+_153573726.23-P1P2|P…
## 19 SUPT5H 11 0.431 5265. SUPT5H_-_39936298.23-P1P2|SUP…
## 20 TAF3 58 0.431 7015. TAF3_-_7860714.23-P1P2|TAF3_-…df_targets %>%
select(target_gene, n_cells, pct_distal, mean_umis_total, sgID_AB) %>%
slice_min(pct_distal, n=20)## # A tibble: 20 × 5
## target_gene n_cells pct_distal mean_umis_total sgID_AB
## <chr> <int> <dbl> <dbl> <chr>
## 1 CPSF6 27 0.378 8465. CPSF6_+_69633592.23-P1P2|CPSF…
## 2 NUDT21 17 0.381 9848. NUDT21_+_56485197.23-P1P2|NUD…
## 3 OGFOD1 55 0.390 9166. OGFOD1_-_56485723.23-P1P2|OGF…
## 4 HSPA5 75 0.403 8014. HSPA5_+_128003614.23-P1P2|HSP…
## 5 NKAP 83 0.404 10176. NKAP_-_119077682.23-P1P2|NKAP…
## 6 COPG1 3 0.405 10665. COPG1_-_128968486.23-P1|COPG1…
## 7 EIF3D 55 0.405 14107. EIF3D_+_36925166.23-P1P2|EIF3…
## 8 CNOT1 99 0.405 15223. CNOT1_+_58663696.23-P1P2|CNOT…
## 9 EIF3E 50 0.405 14643. EIF3E_+_109260706.23-P1P2|EIF…
## 10 NUP93 2 0.405 9407. NUP93_-_56764101.23-P1P2|NUP9…
## 11 SLU7 78 0.406 10583. SLU7_+_159846048.23-P1P2|SLU7…
## 12 RBM33 98 0.406 10426. RBM33_-_155437595.23-P1P2|RBM…
## 13 GAPDH 96 0.406 9675. GAPDH_-_6643761.23-P1|GAPDH_-…
## 14 PSMB6 43 0.406 6931. PSMB6_-_4699495.23-P1|PSMB6_-…
## 15 CDC5L 71 0.407 13840. CDC5L_-_44355475.23-P1P2|CDC5…
## 16 EIF3A 62 0.407 10741. EIF3A_+_120840264.23-P1P2|EIF…
## 17 THOC3 70 0.408 9584. THOC3_-_175395258.23-P1P2|THO…
## 18 RACK1 13 0.408 6820. GNB2L1_+_180670882.23-P1P2|GN…
## 19 EIF3B 123 0.408 10157. EIF3B_-_2394542.23-P1P2|EIF3B…
## 20 POLR3D 31 0.408 7987. POLR3D_-_22102673.23-P1P2|POL…Pair Plots
Proximal vs Distal
g <- df_targets %>%
ggplot(aes(x=pct_proximal, y=pct_distal, color=gene_set, text=target_gene)) +
geom_point(aes(size=n_cells), pch=16) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=0.1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=0.1)) +
theme_bw() +
theme(legend.position="none") +
labs(x="Percent UMIs from proximal isoforms",
y="Percent UMIs from distal isoforms",
color="Gene Set")
ggplotly(g, tooltip=c("text", "x", "y", "gene_set", "n_cells"))df_targets %>%
ggplot(aes(x=pct_proximal, y=pct_distal, color=gene_set, text=target_gene)) +
geom_point(data=filter(df_targets, gene_set == 'other'), size=0.5) +
geom_point(data=filter(df_targets, gene_set == 'non-targeting'), size=0.5) +
geom_point(data=filter(df_targets, is_known), size=3) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=0.1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=0.1)) +
theme_bw() +
labs(x="Percent UMIs from proximal isoforms",
y="Percent UMIs from distal isoforms",
color="Gene Set")
Similar to the first pass, we observed three extreme outliers that have isoform switching effects: CPSF6, NUDT21, and OGFOD1. In the same direction - shortening when knocked down - we see CNOT1 and PABPC1, but with milder effect sizes. In the opposite direction - lengthening when knocked-down - we see PCF11, SRSF7, FIP1L1, and NPAT.
Other factors appear to impact the isoforms almost independently. Namely, knockdown of TAF genes have a strong increase in distal isoforms. Similarly, core factors, such as, CPSF1-4, CSTF1, and CSTF3.
The PAF1 complex members form an outlier set that reduces proximial isoform usage with little impact on distal usage, such as, RTF1, PAF1, and CTF9.
IPA vs Proximal
g <- df_targets %>%
ggplot(aes(x=pct_ipa, y=pct_proximal, color=gene_set, text=target_gene)) +
geom_point(aes(size=n_cells), pch=16) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=0.1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=0.1)) +
theme_bw() +
theme(legend.position="none") +
labs(x="Percent UMIs from IPA isoforms",
y="Percent UMIs from proximal isoforms",
color="Gene Set")
ggplotly(g, tooltip=c("text", "x", "y", "gene_set", "n_cells"))df_targets %>%
ggplot(aes(x=pct_ipa, y=pct_proximal, color=gene_set, text=target_gene)) +
geom_point(data=filter(df_targets, gene_set == 'other'), size=0.5) +
geom_point(data=filter(df_targets, gene_set == 'non-targeting'), size=0.5) +
geom_point(data=filter(df_targets, is_known), size=3) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=0.1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=0.1)) +
theme_bw() +
labs(x="Percent UMIs from IPA isoforms",
y="Percent UMIs from proximal isoforms",
color="Gene Set")
Again, the isoform switchers from above are extreme outliers. Here we see that knockdown of PAF1 complex members reduces proximal usage in favor of increased IPA usage.
IPA vs Distal
g <- df_targets %>%
ggplot(aes(x=pct_ipa, y=pct_distal, color=gene_set, text=target_gene)) +
geom_point(aes(size=n_cells), pch=16) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=0.1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=0.1)) +
theme_bw() +
theme(legend.position="none") +
labs(x="Percent UMIs from IPA isoforms",
y="Percent UMIs from distal isoforms",
color="Gene Set")
ggplotly(g, tooltip=c("text", "x", "y", "gene_set", "n_cells"))df_targets %>%
ggplot(aes(x=pct_ipa, y=pct_distal, color=gene_set, text=target_gene)) +
geom_point(data=filter(df_targets, gene_set == 'other'), size=0.5) +
geom_point(data=filter(df_targets, gene_set == 'non-targeting'), size=0.5) +
geom_point(data=filter(df_targets, is_known), size=3) +
geom_rug(size=0.1) +
scale_color_manual(values=COLOR_MAP) +
scale_size_continuous(range=c(0.05, 3)) +
scale_x_continuous(labels=scales::percent_format(accuracy=0.1)) +
scale_y_continuous(labels=scales::percent_format(accuracy=0.1)) +
theme_bw() +
labs(x="Percent UMIs from IPA isoforms",
y="Percent UMIs from distal isoforms",
color="Gene Set")
Here we observe the factors whose knockdown increased distal usage independent of proximal usage do so at the cost of decreased IPA usage. These were the TAF members, CPSF1-4, and CSTF3. Notably, CPSF3 does not appear to impact IPA.
Conclusion
Considering on multi-UTR genes provides a cleaner signal for effects on different isoforms. We now see many of the known factors stand out, and can identify other possible genes with isoform-specific effects.
Runtime Details
Session Info
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/mfansler/miniconda3/envs/bioc_3_14/lib/libopenblasp-r0.3.18.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
## [3] Biobase_2.54.0 GenomicRanges_1.46.0
## [5] GenomeInfoDb_1.30.0 IRanges_2.28.0
## [7] S4Vectors_0.32.0 BiocGenerics_0.40.0
## [9] MatrixGenerics_1.6.0 matrixStats_0.61.0
## [11] Matrix_1.3-4 ggbeeswarm_0.6.0
## [13] plotly_4.10.0 forcats_0.5.1
## [15] stringr_1.4.0 dplyr_1.0.8
## [17] purrr_0.3.4 readr_2.1.1
## [19] tidyr_1.1.4 tibble_3.1.7
## [21] ggplot2_3.3.5 tidyverse_1.3.1
## [23] magrittr_2.0.3
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 fs_1.5.2 lubridate_1.8.0
## [4] httr_1.4.2 tools_4.1.1 backports_1.4.0
## [7] bslib_0.3.1 utf8_1.2.2 R6_2.5.1
## [10] vipor_0.4.5 DBI_1.1.1 lazyeval_0.2.2
## [13] colorspace_2.0-2 withr_2.4.3 tidyselect_1.1.1
## [16] compiler_4.1.1 cli_3.3.0 rvest_1.0.2
## [19] xml2_1.3.3 DelayedArray_0.20.0 labeling_0.4.2
## [22] sass_0.4.0 scales_1.1.1 digest_0.6.29
## [25] rmarkdown_2.11 XVector_0.34.0 pkgconfig_2.0.3
## [28] htmltools_0.5.2 highr_0.9 dbplyr_2.1.1
## [31] fastmap_1.1.0 htmlwidgets_1.5.4 rlang_1.0.2
## [34] readxl_1.3.1 rstudioapi_0.13 farver_2.1.0
## [37] jquerylib_0.1.4 generics_0.1.1 jsonlite_1.7.2
## [40] crosstalk_1.2.0 RCurl_1.98-1.5 GenomeInfoDbData_1.2.7
## [43] Rcpp_1.0.7 munsell_0.5.0 fansi_0.5.0
## [46] lifecycle_1.0.1 stringi_1.7.6 yaml_2.2.1
## [49] zlibbioc_1.40.0 grid_4.1.1 crayon_1.4.2
## [52] lattice_0.20-45 haven_2.4.3 hms_1.1.1
## [55] knitr_1.39 pillar_1.7.0 reprex_2.0.1
## [58] glue_1.6.2 evaluate_0.15 data.table_1.14.2
## [61] modelr_0.1.8 vctrs_0.4.1 tzdb_0.2.0
## [64] cellranger_1.1.0 gtable_0.3.0 assertthat_0.2.1
## [67] xfun_0.30 broom_0.8.0 viridisLite_0.4.0
## [70] beeswarm_0.4.0 ellipsis_0.3.2Conda Environment
## Conda Environment YAML
name: base
channels:
- conda-forge
- bioconda
- defaults
dependencies:
- anaconda-client=1.8.0=pyhd8ed1ab_0
- anaconda-project=0.10.2=pyhd8ed1ab_0
- attrs=21.2.0=pyhd8ed1ab_0
- awscli=1.25.79=py39h6e9494a_0
- backports=1.0=py_2
- backports.functools_lru_cache=1.6.4=pyhd8ed1ab_0
- backports.zoneinfo=0.2.1=py39h701faf5_5
- bagit=1.8.1=pyhd8ed1ab_0
- bagit-profile=1.3.1=pyhd8ed1ab_0
- bdbag=1.6.1=pyhd8ed1ab_0
- beautifulsoup4=4.9.3=pyhb0f4dca_0
- blinker=1.4=py_1
- boa=0.13.0=pyha770c72_0
- boolean.py=3.7=py_0
- boto3=1.24.78=pyhd8ed1ab_0
- botocore=1.27.78=pyhd8ed1ab_0
- brotlipy=0.7.0=py39h63b48b0_1004
- bzip2=1.0.8=h0d85af4_4
- c-ares=1.18.1=h0d85af4_0
- ca-certificates=2022.9.24=h033912b_0
- cachecontrol=0.12.11=pyhd8ed1ab_0
- cairo=1.16.0=he43a7df_1008
- cctools=973.0.1=hd9211c8_2
- cctools_osx-64=973.0.1=h3e07e27_2
- certifi=2022.9.24=pyhd8ed1ab_0
- cffi=1.15.1=py39hae9ecf2_0
- chardet=5.0.0=py39h6e9494a_0
- charset-normalizer=2.0.0=pyhd8ed1ab_0
- click=8.1.3=py39h6e9494a_0
- clyent=1.2.2=py_1
- colorama=0.4.3=py_0
- commonmark=0.9.1=py_0
- conda=4.14.0=py39h6e9494a_0
- conda-build=3.21.9=py39h6e9494a_1
- conda-forge-pinning=2021.10.10.22.03.30=hd8ed1ab_0
- conda-libmamba-solver=22.6.0=pyhd8ed1ab_0
- conda-pack=0.6.0=pyhd3deb0d_0
- conda-package-handling=1.9.0=py39ha30fb19_0
- conda-smithy=3.17.2=pyhd8ed1ab_0
- conda-standalone=4.10.3=h694c41f_0
- conda-suggest=0.1.1=pyh9f0ad1d_0
- conda-suggest-conda-forge=2021.8.24=h694c41f_0
- conda-verify=3.1.1=py39h6e9494a_1004
- constructor=3.3.1=py39h6e9494a_0
- cryptography=38.0.3=py39h7eb6a14_0
- curl=7.86.0=h57eb407_1
- dataclasses=0.8=pyhc8e2a94_3
- dbus=1.13.6=ha13b53f_2
- deprecated=1.2.12=pyh44b312d_0
- docutils=0.16=py39h6e9494a_3
- expat=2.4.1=he49afe7_0
- ffq=0.2.1=pyhdfd78af_0
- filelock=3.0.12=pyh9f0ad1d_0
- fmt=9.1.0=hb8565cd_0
- font-ttf-dejavu-sans-mono=2.37=hab24e00_0
- font-ttf-inconsolata=3.000=h77eed37_0
- font-ttf-source-code-pro=2.038=h77eed37_0
- font-ttf-ubuntu=0.83=hab24e00_0
- fontconfig=2.13.1=h10f422b_1005
- fonts-conda-ecosystem=1=0
- fonts-conda-forge=1=0
- freetype=2.10.4=h4cff582_1
- fribidi=1.0.10=hbcb3906_0
- frozendict=2.3.4=py39h701faf5_0
- future=0.18.2=py39h6e9494a_5
- gawk=5.1.0=h8a989fb_0
- gdk-pixbuf=2.42.6=h2e6141f_0
- gettext=0.21.1=h8a4c099_0
- giflib=5.2.1=hbcb3906_2
- git=2.34.1=pl5321h9a53687_0
- git-lfs=2.13.3=h694c41f_0
- gitdb=4.0.7=pyhd8ed1ab_0
- gitpython=3.1.18=pyhd8ed1ab_0
- glib=2.70.2=hcf210ce_0
- glib-tools=2.70.2=hcf210ce_0
- glob2=0.7=py_0
- globus-sdk=2.0.1=pyhd8ed1ab_0
- gmp=6.2.1=h2e338ed_0
- gnutls=3.6.13=h756fd2b_1
- graphite2=1.3.13=h2e338ed_1001
- harfbuzz=2.9.0=h159f659_0
- htop=3.2.1=h398481e_0
- htslib=1.15=hc057d7f_0
- hub=2.14.2=hc7d050b_0
- icu=68.1=h74dc148_0
- idna=3.1=pyhd3deb0d_0
- importlib-metadata=4.11.4=py39h6e9494a_0
- importlib_metadata=4.11.4=hd8ed1ab_0
- importlib_resources=5.4.0=pyhd8ed1ab_0
- inotify_simple=1.3.5=pyha770c72_3
- ipython_genutils=0.2.0=py_1
- isodate=0.6.0=py_1
- jbig=2.1=h0d85af4_2003
- jinja2=3.0.1=pyhd8ed1ab_0
- jmespath=0.10.0=pyh9f0ad1d_0
- joblib=1.0.1=pyhd8ed1ab_0
- jpeg=9d=hbcb3906_0
- json5=0.9.5=pyh9f0ad1d_0
- jsonschema=4.3.1=pyhd8ed1ab_0
- jupyter_core=4.11.1=py39h6e9494a_0
- krb5=1.19.3=hb49756b_0
- ld64=609=hd2e7500_2
- ld64_osx-64=609=h2487922_2
- ldid=2.1.2=h6a69015_3
- lerc=2.2.1=h046ec9c_0
- libarchive=3.5.2=h2b60450_1
- libcurl=7.86.0=h57eb407_1
- libcxx=14.0.6=hccf4f1f_0
- libdeflate=1.10=h0d85af4_0
- libedit=3.1.20191231=h0678c8f_2
- libev=4.33=haf1e3a3_1
- libffi=3.4.2=h0d85af4_5
- libglib=2.70.2=hf1fb8c0_0
- libiconv=1.17=hac89ed1_0
- libidn2=2.3.2=h0d85af4_0
- liblief=0.11.5=he49afe7_0
- libllvm12=12.0.1=hd011deb_2
- libmamba=1.0.0=h2bf831e_2
- libmambapy=1.0.0=py39he069e75_2
- libnghttp2=1.47.0=h7cbc4dc_1
- libpng=1.6.37=h7cec526_2
- librsvg=2.50.7=hd2a7919_0
- libsolv=0.7.22=hd9580d2_0
- libssh2=1.10.0=h7535e13_3
- libtiff=4.3.0=h1167814_0
- libunistring=0.9.10=h0d85af4_0
- libwebp-base=1.2.1=h0d85af4_0
- libxml2=2.9.12=h93ec3fd_0
- libxslt=1.1.33=h5739fc3_2
- libzlib=1.2.13=hfd90126_4
- license-expression=1.2=py_0
- lockfile=0.12.2=py_1
- lxml=4.8.0=py39h63b48b0_2
- lz4-c=1.9.3=he49afe7_1
- lzo=2.10=haf1e3a3_1000
- mamba=1.0.0=py39ha435c47_2
- markupsafe=2.1.1=py39h63b48b0_1
- msgpack-python=1.0.4=py39h92daf61_1
- msrest=0.6.21=pyh44b312d_0
- nbformat=5.1.3=pyhd8ed1ab_0
- ncurses=6.3=h96cf925_1
- nettle=3.6=hedd7734_0
- oauthlib=3.1.1=pyhd8ed1ab_0
- openssl=1.1.1s=hfd90126_0
- pango=1.48.9=ha05cd14_0
- patch=2.7.6=hbcf498f_1002
- pcre=8.45=he49afe7_0
- pcre2=10.37=ha16e1b2_0
- perl=5.32.1=0_h0d85af4_perl5
- pigz=2.6=h5dbffcc_0
- pip=21.2.4=pyhd8ed1ab_0
- pixman=0.40.0=hbcb3906_0
- pkginfo=1.7.1=pyhd8ed1ab_0
- popt=1.16=h7b079dc_2002
- prompt-toolkit=3.0.20=pyha770c72_0
- prompt_toolkit=3.0.20=hd8ed1ab_0
- psutil=5.9.2=py39ha30fb19_0
- py-lief=0.11.5=py39h9fcab8e_0
- pyasn1=0.4.8=py_0
- pybind11-abi=4=hd8ed1ab_3
- pycosat=0.6.3=py39h63b48b0_1010
- pycparser=2.20=pyh9f0ad1d_2
- pycrypto=2.6.1=py39h89e85a6_1006
- pygithub=1.53=py_0
- pygments=2.10.0=pyhd8ed1ab_0
- pyjwt=1.7.1=py_0
- pyrsistent=0.18.1=py39h63b48b0_1
- pysocks=1.7.1=pyha2e5f31_6
- python=3.9.13=h57e37ff_0_cpython
- python-dateutil=2.8.2=pyhd8ed1ab_0
- python-libarchive-c=4.0=py39h6e9494a_1
- python-tzdata=2021.5=pyhd8ed1ab_0
- python_abi=3.9=2_cp39
- pytz=2021.1=pyhd8ed1ab_0
- pytz-deprecation-shim=0.1.0.post0=py39h6e9494a_2
- pyyaml=5.4.1=py39h701faf5_3
- readline=8.1.2=h3899abd_0
- reproc=14.2.3=h0d85af4_0
- reproc-cpp=14.2.3=he49afe7_0
- requests=2.28.1=pyhd8ed1ab_1
- requests-oauthlib=1.3.0=pyh9f0ad1d_0
- rich=10.16.1=pyhd8ed1ab_0
- ripgrep=13.0.0=h244e342_0
- rsa=4.7.2=pyh44b312d_0
- rsync=3.2.7=ha1fed10_0
- ruamel.yaml=0.17.21=py39h63b48b0_1
- ruamel.yaml.clib=0.2.6=py39h63b48b0_1
- ruamel_yaml=0.15.80=py39h701faf5_1007
- s3transfer=0.6.0=pyhd8ed1ab_0
- scrypt=0.8.18=py39hbfd427f_4
- setuptools=65.3.0=pyhd8ed1ab_1
- six=1.16.0=pyh6c4a22f_0
- smartmontools=7.2=h940c156_0
- smmap=3.0.5=pyh44b312d_0
- soupsieve=2.3.1=pyhd8ed1ab_0
- sqlite=3.38.5=hd9f0692_0
- tapi=1100.0.11=h9ce4665_0
- tk=8.6.12=h5dbffcc_0
- toolz=0.11.1=py_0
- tornado=6.2=py39h701faf5_0
- tqdm=4.62.2=pyhd8ed1ab_0
- traitlets=5.1.0=pyhd8ed1ab_0
- typing_extensions=3.10.0.0=pyha770c72_0
- tzdata=2021e=he74cb21_0
- tzlocal=4.2=py39h6e9494a_1
- urllib3=1.26.6=pyhd8ed1ab_0
- vsts-python-api=0.1.22=py_0
- watchgod=0.7=pyhd8ed1ab_0
- wcwidth=0.2.5=pyh9f0ad1d_2
- wget=1.20.3=h52ee1ee_1
- wheel=0.37.0=pyhd8ed1ab_1
- wrapt=1.14.1=py39h701faf5_0
- xxhash=0.8.0=h35c211d_3
- xz=5.2.5=haf1e3a3_1
- yaml=0.2.5=haf1e3a3_0
- yaml-cpp=0.7.0=hb486fe8_1
- zipp=3.5.0=pyhd8ed1ab_0
- zlib=1.2.13=hfd90126_4
- zstd=1.5.2=hfa58983_4
- pip:
- pyopenssl==20.0.1
prefix: /Users/mfansler/miniconda3